Before You Automate: An AI Project Checklist

Write the task as an input and an output

For example, a team may need to classify incoming requests, summarize approved documents or draft a response for review. Each task needs different evidence and permissions.

Specify what a correct output looks like and which mistakes would matter. If the success criteria are vague, evaluation will be vague too.

Separate rules from interpretation

Deterministic rules are usually appropriate for calculations, required fields and access checks. AI can help interpret language or classify uncertain content, but it should not silently replace controls that need predictable behavior.

A mixed workflow often works better than putting every step behind a model. Keep the boundaries understandable to the team operating it.

Decide what requires a person

Sending a message, changing a business record or publishing content can have consequences beyond the model’s answer. Define approvals and permissions for those actions.

When information is missing, the system should ask, stop or escalate according to an explicit rule. Confidently guessing is not a recovery plan.

Test failures before expansion

Build an evaluation set from representative cases, including incomplete, conflicting and out-of-scope inputs. Record failures and how the workflow handles them.

Start with a narrow rollout, monitor the behavior and retain a manual path. Expand only when the system demonstrates the quality and control the task needs.

Make the next decision concrete

Write down the requirement, the evidence you already have and what remains uncertain. A useful project conversation starts with that context—not a list of features copied from another business.

For implementation support, explore AI Development, AI Business Automation, AI Integrations. You can also share your project with Web Synergy.